Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment
Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment
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DOI：
10.1016/j.agrformet.2011.08.002
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发表时间：
2011-12-15
影响因子：
6.2
通讯作者：
Defourny, Pierre
中科院分区：
文献类型：
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作者：
Curnel, Yannick;de Wit, Allard J. W.;Defourny, Pierre
通过观测系统模拟试验（OSSE），研究了将冬小麦叶面积指数（LAI）遥感估算值同化到作物生长模型WOFOST中的可能性。两种同化策略被认为是：一个基于Enhancement卡尔曼滤波器（EnKF）和第二个不确定的模型参数和初始状态条件的重新校准/重新初始化。OSS实验的主要目的是估计的精度和采样频率，以达到25%或50%的最终估计的粮食yield.Our结果表明，EnKF是不适合同化叶面积指数在WOFOST最终估计的平均误差增加的目标。这些不佳的结果可以解释为可能存在的差异的物候发育同化和模拟的LAI值（差异在我们的研究中被称为“物候转变”），这是不正确的EnKF为基础的同化策略。相反，一个重新校准为基础的同化方法在全球范围内提高了最终的粮食产量的估计在一个显着的方式。平均而言，这种改善可以达到约65%时，观察所有沿着生长季节。在季节的早期已经可以实现20%的改进，这在作物产量预测方面具有很大的意义。如果在相当多的同化叶面积指数观测可用性和不确定性水平下可以达到最终谷物产量误差减少的第一个目标（25%），则第二个目标（50%）的可能性范围将受到显着限制，这意味着在整个生长季节沿着都有可用的叶面积指数观测，至少每周一次，并且不确定性水平等于或理想地低于10%。从技术和操作的角度来看，这些要求目前都没有得到满足，但这里介绍的结果可以为今后专门用于作物生长监测的任务提供指导。(C)2011 Elsevier B. V.保留所有权利。
An Observing System Simulation Experiment (OSSE) has been defined to assess the potentialities of assimilating winter wheat leaf area index (LAI) estimations derived from remote sensing into the crop growth model WOFOST. Two assimilation strategies are considered: one based on Ensemble Kalman Filter (EnKF) and the second on recalibration/re-initialisation of uncertain model parameters and initial state conditions. The main objective of the OSS Experiment is to estimate the requisites for the remotely sensed LAI, in terms of accuracy and sampling frequency, to reach target of either 25 or 50% reduction of errors on the final estimation of grain yields.Our results demonstrate that EnKF is not suitable for assimilating LAI in WOFOST as the average error on final grain yields estimation globally increases. These poor results can be explained by the possible differences of phenological development existing between assimilated and modelled LAI values (difference called "phenological shift" in our study) which is not corrected by the EnKF-based assimilation strategy.On the contrary, a recalibration-based assimilation approach globally improves the estimation of final grain yields in a significant way. On average, such improvement can reach up to approximately 65% when observations are available all along the growing season. Improvements on the order of 20% can be already be attained early in the season, which is of great interest in a crop yield forecasting perspective. If the first objective (25%) of error reduction on final grain yields can be reached in a quite high number of assimilated LAI observations availabilities and uncertainty levels, the field of possibilities is significantly restricted for the second objective (50%) and implies to have LAI observations available all along the growing season, at least on a weekly basis and with an uncertainty level equal or ideally lower than 10%. These requirements are not currently met from neither a technological nor an operational point of view but the results presented here can provide guidelines for future missions dedicated to crop growth monitoring. (C) 2011 Elsevier B.V. All rights reserved.